Influence of Pore Size on Carbon Dioxide Diffusion in Two Isoreticular Metal–Organic Frameworks
Bibliographic record
Abstract
The rapid diffusion of molecules in porous materials is critical for numerous applications including separations, energy storage, sensing, and catalysis. A common strategy for tuning guest diffusion rates is to vary the material pore size, although detailed studies that isolate the effect of changing this particular variable are lacking. Here, we begin to address this challenge by measuring the diffusion of carbon dioxide in two isoreticular metal–organic frameworks featuring channels with different diameters, Zn 2 (dobdc) (dobdc 4– = 2,5-dioxidobenzene-1,4-dicarboxylate) and Zn 2 (dobpdc) (dobpdc 4− = 4,4′-dioxidobiphenyl-3,3′-dicarboxylate), using pulsed field gradient NMR spectroscopy. An increase in the pore diameter from 15 Å in Zn 2 (dobdc) to 22 Å in Zn 2 (dobpdc) is accompanied by an increase in the self-diffusion of CO 2 by a factor of 4 to 6, depending on the gas pressure. Analysis of the diffusion anisotropy in Zn 2 (dobdc) reveals that the self-diffusion coefficient for motion of CO 2 along the framework channels is at least 10,000 times greater than for motion between the framework channels. Our findings should aid the design of improved porous materials for a range of applications where diffusion plays a critical role in determining performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".